Personalizing COPD therapies by determining heterogeneity of treatment effects Project Summary/Abstract Chronic obstructive pulmonary disease (COPD) is the 3rd leading cause of death in the country, affecting nearly 24 million Americans1. COPD is a heterogeneous disease2, making it difficult to personalize treatment decisions. Existing treatment recommendations3 are heavily influenced by severity of airflow obstruction, a poor predictor of outcomes such as exacerbations and death3. Multidimensional indices to predict disease outcomes4,5 and treatment recommendations account for exacerbations and dyspnea3, but do not account for comorbidities, frailty and age-related conditions that are common in COPD. For example, two patients with similar FEV1% predicted and exacerbation frequency will receive the same therapy, regardless of risk for cardiovascular events with long-acting bronchodilators6 or pneumonia with inhaled steroids7,8. Indeed, the recommendations overlook that there are likely to be individuals who will benefit more or less from certain therapies than the average outcome reported from randomized clinical trials (RCTs). This heterogeneity of treatment effects is a novel concept9,10, explained by the fact that RCTs report average results for the average participant, ignoring differences in baseline risk10 and the possibility that treatment benefit varies by baseline risk. Indeed, with a constant relative risk reduction across baseline risk, the absolute benefit of treatment will be lower for te lowest risk patients, so that those at lowest risk may have more adverse than beneficial effects from treatment. This concept, not previously explored in COPD, is relevant to identifying appropriate candidates for different COPD therapies. For example, although azithromycin reduced exacerbations in a RCT11, it has not been adopted by treatment guidelines3 due to concerns about side effects12,13. Subgroup analysis and research on COPD phenotypes have identified patient characteristics impacting outcomes with azithromycin and roflumilast14,15; however, evaluating heterogeneity of treatment effects using multivariate models will provide more nuanced information to guide individual treatment, as has been demonstrated for other diseases16,17. Similarly, existing treatment guidelines and multidimensional indices do not address the growing evidence of premature aging in COPD18-20, an important consideration as COPD patients frequently have impairments in vision, hearing, memory, incontinence, falls, sarcopenia and are at risk for dependency for activities of daily living (all hereafter termed geriatric conditions21). These factors change the baseline risk and are probable contributors to heterogeneity of treatment response22, but have been, so far, overlooked. Most geriatric conditions can be assessed in the office, and sarcopenia can be assessed in clinically-indicated thoracic imaging studies. Incorporating geriatric conditions into models of heterogeneity of treatment effects will allow us to maximize treatment benefits for individual COPD patients23,24. Aligned with the NHLBI goal of translating research into practice, we propose to re-analyze RCTs of COPD treatments to identify heterogeneity of treatment effects and test the hypothesis that adding data on geriatric conditions will yield robust and applicable personalized risk models. We hypothesize that using multivariate models of COPD outcomes will allow us to identify individuals most likely to benefit and less likely to experience harm from COPD interventions and that geriatric conditions will independently contribute to risk of poor COPD outcomes, and that inclusion of geriatric conditions will result in more robust risk models. This patient-orientd proposal has two key objectives. First, we will use multivariate models of COPD outcomes to re-analyze RCTs to assess heterogeneity of treatment effects, to estimate how the benefits and risks of COPD treatments vary for individuals. Second, we will prospectively determine the burden of geriatric conditions in COPD and test their contribution to more accurate risk models of outcomes, which can be used in the future for similar analysis and to design more efficient and informative RCTs. Our specific aims, focused on individuals ?50 years old with COPD, are: 1) Use risk models of COPD outcomes to identify heterogeneity of treatment effects in COPD; 2) Determine the frequency of geriatric conditions in COPD; and 3) Determine the independent contribution of geriatric conditions to 2-year COPD outcomes. Completion of these studies will generate novel and robust models of COPD outcomes, allowing for identification of subjects most likely to benefit or experience harm from interventions; a first step in developing personalized decisions. Dr. Martinez has a unique background as a Pulmonologist trained in Public Health, Health Services and Translational Research, with a proven commitment to research and scholarly productivity. This proposal includes an educational plan that will allow him to develop the gaps in his training: focused expertise in novel analysis of clinical trials and longitudinal studies, geriatric aspects of pulmonary disease, and outcome prediction. These experiences will provide him with the foundation to become an independent investigator in translational research with emphasis on the elderly and personalized medicine. Dr. Martinez will be supported by an outstanding multi-disciplinary mentorship team: Dr. Sandeep Vijan, a pioneer in developing models to inform the delivery of personalized medicine and Dr. MeiLan K. Han, a leader in COPD phenotyping. Drs. Neil B. Alexander, Caroline R. Richardson, and Christine T. Cigolle, leaders in geriatrics, and Dr. Fernando Martinez, a leader in developing treatments for COPD, will serve as advisors.